385 citations · 1.7k across the 35 of their papers we have counts for
44 papers
Gradient-Based Spectral Embeddings of Random Dot Product Graphs
Marcelo Fiori, Bernardo Marenco, Federico Larroca +2
The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially po…
Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features
Saurabh Sihag, Gonzalo Mateos, Corey T. McMillan +1
Graph convolutional networks (GCN) leverage topology-driven graph convolutional operations to combine information across the graph for inference tasks. In our recent work, we have…
Explainable Brain Age Prediction using coVariance Neural Networks
Saurabh Sihag, Gonzalo Mateos, Corey McMillan +1
In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of "brain age" f…
Fairness-Aware Graph Filter Design
O. Deniz Kose, Yanning Shen, Gonzalo Mateos
Graphs are mathematical tools that can be used to represent complex real-world systems, such as financial markets and social networks. Hence, machine learning (ML) over graphs has…
Dual-based Online Learning of Dynamic Network Topologies
Seyed Saman Saboksayr, Gonzalo Mateos
We investigate online network topology identification from smooth nodal observations acquired in a streaming fashion. Different from non-adaptive batch solutions, our distinctive g…
pyGSL: A Graph Structure Learning Toolkit
Max Wasserman, Gonzalo Mateos
We introduce pyGSL, a Python library that provides efficient implementations of state-of-the-art graph structure learning models along with diverse datasets to evaluate them on. Th…